– Introduction
The ambition of the GPW13 is to improve the health of billions of people in the next five years. The WHO Impact Measurement Framework is part of GPW13. It measures progress at three levels:
- 46 outcome indicators and their global targets for 2023, covering a range of health issues
- The Triple Billion targets, to be achieved by 2023:
- billion more people benefiting from universal health coverage
- billion more people better protected from health emergencies
- billion more people enjoying better health and well-being.
- Healthy life expectancy (HALE), quantifying expected years of life in good health as a measure of the overall health of populations.
The purpose of this document is to be a live document that summarises the data “in hand” for the 46 outcome indicators i.e. the data that have been submitted to the DDI via the Global Health Observatory (GHO) or through other means. Based on either the estimates as generated by technical programmes or according to a set of estimation/extrapolation methodologies (with statistical assumptions and R-code provided), the document explores what estimates of baseline and projection values can be determined for each of the 46 indicators using the data we have and also highlights the indicators for which data gaps restrict robust estimation and extrapolation.
Detailed information on the methods of the GPW13 is available for download in the methods report which can be obtained from here,
– Table of indicators
The 46 outcome indicators are listed in the table below and can be downloaded from here. For each of these indicators, the ones highlighted in orange are included in the corresponding billions calculation whereas the ones highlighted in green are relevant to the billions but not directly included in the calculation.
Show Indicator Table Hide Indicator Table| GHO code | Indicator | UHC | HEP | HPOP | |
|---|---|---|---|---|---|
|
SDGDISASTER | Number of persons affected by disasters (per 100 000 population) | Relevant | ||
|
GHED_GGHE-DGGE_SHA2011 | Domestic general government health expenditure (GGHE-D) (% of general government expenditure (GGE)) | Relevant | ||
|
NUTRITION_HA_2 | Prevalence of stunting in children under 5 (%) | Included | ||
|
NUTRITION_WH_2 | Prevalence of wasting in children under 5 (%) | Included | ||
|
NUTRITION_WH2 | Prevalence of overweight in children under 5 (%) | Included | ||
|
MDG_0000000026 | Maternal mortality ratio (per 100 000 live births) | Relevant | ||
|
MDG_0000000025 | Proportion of births attended by skilled health personnel (%) | Relevant | ||
|
MDG_0000000007 | Under-five mortality rate (per 1000 live births) | Relevant | ||
|
WHOSIS_000003 | Neonatal mortality rate (per 1000 live births) | Relevant | ||
|
SDGHIV | New HIV infections (per 1000 uninfected population) | Relevant | ||
|
MDG_0000000020 | Tuberculosis incidence (per 100 000 population) | Relevant | ||
|
SDGMALARIA | Malaria incidence (per 1000 population at risk) | Relevant | ||
|
SDGHEPHBSAGPRV | Hepatitis B incidence (measured by: surface antigen (HBsAg) prevalence among children under 5 years) | Relevant | ||
|
SDGNTDTREATMENT | Number of people requiring interventions against NTDs | Relevant | ||
|
NCDMORT3070 | Probability of dying from any of CVD, cancer, diabetes, CRD (aged 30–70) (%) | Relevant | ||
|
SDGSUICIDE | Suicide mortality rate (per 100 000 population) | Included | ||
|
Coverage of treatment interventions for substance-use disorders (%) | Relevant | |||
|
SA_0000001688 | Total alcohol per capita consumption in adults aged 15+ (litres of pure alcohol) | Included | ||
|
RS_198 | Road traffic mortality rate (per 100 000 population) | Included | ||
|
SDGFPALL | Proportion of women (aged 15–49) having need for family planning satisfied with modern methods (%) | Included | ||
|
UHC_INDEX_REPORTED | UHC Service Coverage Index | Included | ||
|
FINPROTECTION_CATA_TOT_10_POP | Population with household expenditures on health >10% of total household expenditure or income (%) | Included | ||
|
SDGAIRBODA | Mortality rate attributed to air pollution (per 100 000 population) | Relevant | ||
|
SDGWSHBOD | Mortality rate attributed to exposure to unsafe WASH services (per 100 000 population) | Relevant | ||
|
SDGPOISON | Mortality rate from unintentional poisoning (per 100 000 population) | Relevant | ||
|
SDGTOBACCO | Prevalence of tobacco use in adults aged 15+ (%) | Included | Included | |
|
WHS4_100 | Proportion of population covered by all vaccines included in national programmes (DTP3, MCV2, PCV3) (%) | Included | Relevant | |
|
SDGHEALTHFACILITIESESSENTIALMEDS | Proportion of health facilities with essential medicines available and affordable on a sustainable basis (%) | Relevant | ||
|
HWF_0001 | Density of health workers (doctors; nurse and midwives; pharmacists; dentists per 10 000 population) | Included | ||
|
SDGIHR2018 | International Health Regulations (IHR) capacity and health emergency preparedness | Included | Included | |
|
Proportion of bloodstream infections due to antimicrobial resistant organisms (%) | Relevant | |||
|
SE_DEV_ONTRK | Proportion of children under 5 developmentally on track (health, learning and psychosocial well-being) (%) | Included | ||
|
SDGIPV | Proportion of women (aged 15–49) subjected to violence by current or former intimate partner (%) | Included | ||
|
SG_DMK_SRCR_FN_ZS | Proportion of women (aged 15–49) who make their own decisions regarding sexual relations, contraceptive use and reproductive health care (%) | Relevant | ||
|
WSH_WATER_SAFELY_MANAGED | Proportion of population using safely managed drinking water services (%) | Included | ||
|
WSH_SANITATION_SAFELY_MANAGED | Proportion of population using safely managed sanitation services and hand-washing facility (%) | Included | ||
|
SDGPOLLUTINGFUELS | Proportion of population with primary reliance on clean fuels (%) | Included | ||
|
SDGPM25 | Annual mean concentrations of fine particulate matter (PM2.5) in urban areas (µg/m3) | Included | ||
|
CHILDVIOL | Proportion of children (aged 1–17) experiencing physical or psychological aggression (%) | Included | ||
|
Vaccine coverage for epidemic prone diseases | Included | |||
|
Proportion of vulnerable people in fragile settings provided with essential health services (%) | Included | |||
|
BP_04 | Prevalence of raised blood pressure in adults aged 18+ | Included | ||
|
NCD_CCS_SatFat | Effective policy/regulation for industrially produced transfatty acids (TFA) (Y/N) | Included | ||
|
NCD_BMI_30C | Prevalence of obesity (%) | Included | ||
|
WHS3_49 | Number of cases of poliomyelitis caused by wild poliovirus (WPV) | Relevant | ||
|
Patterns of antibiotic consumption at national level | Relevant | |||
| Note: | |||||
| a There are separate indicators for doctors, nurses, midwives and pharmacists in GHO, weighted average? | |||||
| b Sanitation and hand-washing have separate indicators in GHO, weighted average? | |||||
| c Adults and children have separate indicators in GHO, weighted average? | |||||
| * For the HPOP Billion non-age-standardized data are used. | |||||
| † SDG 1.a.2 includes several indicators. Only the one relating to health expenditure is used in GPW13. |
The 6 indicators without a gho_code in the table above are currently not in the GHO. The first do not have any data at all as yet nor do they have any additional info or focal points leading i.e.
- Coverage of treatment interventions for substance-use disorders (%) [UHC, SDG 3.5.1]
- Proportion of bloodstream infections due to antimicrobial resistant organisms (%) [UHC, SDG 3.d.2]
- Patterns of antibiotic consumption at national level [UHC, WHA 68.7]
The remaining three are components of the billions calculation and do not currently have data in GHO (need to follow up as to where they are and length of time-series) i.e.
- Proportion of children (aged 1–17) experiencing physical or psychological aggression (%) [HPOP, SDG 16.2.1] ⭐
- Vaccine coverage for epidemic prone diseases [HEP] ⭐
- Proportion of vulnerable people in fragile settings provided with essential health services (%) [HEP] ⭐
The remaining 40 indicators that are in the GHO can be extracted using the ghost R-package which was designed to provide a simple interface for extracting data from the World Health Organization’s Global Health Observatory (GHO) database using the GHO Open Data Protocol API. The package allows for exploration of indicators and dimensions available in the GHO and extract of these into R data frames.
– Methods for infilling and projections
A systematic estimation approach is used by the WHO to determine baselines and projections where no values are currently available. It incorporates a hierarchical assignment of estimates beginning with obtaining the indicator values that have been uploaded onto the Global Health Observatory, any additional data determined by technical programmes but not stored on the GHO due to formatting or other restrictions, followed by estimation for any years missing for any indicators and/or countries using statistical models. The estimation process itself is conditional on the number of data points available for each indicator and each country. For those with robust historical time-series, indicators are modeled and forecasted using a standard time-series forecast model that accounts for serial correlation. For the indicators for which there are only very few or even no data points, a linear mixed effects regression model that also accounts for temporal correlation, is applied. The mixed effects model predicts the values for the indicators using covariates including a composite sociodemographic index created from country- and year-specific lagged income, total fertility and mean years of education as well as life-expectancy at birth. These methods are described in more detail below.
Forecast for indicators with robust time-series
Where at least 2 observations are available for an indicator an initial step to completing the historical time-series is linear interpolation to obtain any intermediate values. Once completed, the time-series is forecasted under two main correlation assumptions of the indicators over time. These are applied conditional on the general linearity of the historical series and are:
- The autoregressive lag-1 (AR-1) model which can be represented by:
\[y_{i} = \alpha + \rho y_{i-1} + \varepsilon_i\]
where \(y_i\) is the indicator value in year \(t = i\), \(\alpha\) is an intercept, \(\rho\) is and \(\varepsilon_{i}\) is a random error.
- The second order random walk (RW2) model which has the density \[\pi(y) \propto exp\left(-\frac{1}{2}\sum_{i=3}^n (y_i - 2y_{i-1} + y_{i-2})^2\right)\] where the term \(y_{i+1} −2y_i +y_{i−1}\) can be interpreted as an estimate of the second order derivative of a continuous time function \(y(t)\) at \(t = i\) using values of \(y(t)\) at \(t = i − 1\), \(i\), and \(i+1\).
Mixed effects model
For the indicators with a single or no observations, a linear mixed effects model is applied. In a linear mixed effects model we let \(y_j = (y_{j1}; \ldots ; y_{jn})\) denote the vector of repeated measurements for a group \(j\). We reconsider the general model \[ y_j = X_j\beta + Zjbj + \varepsilon_j\] in which \(\beta\) is a vector of population-average regression coefficients called fixed effects, and where \(b_j\) is a vector of group-specific regression coefficients. The \(b_j\) describe how the evolution of the \(j\)th group deviates from the average. The matrices \(X_j\) and \(Z_j\) are matrices of known covariates. The residual components \(\varepsilon_j\) are assumed to be independent \(N(0, \Sigma_j)\), where \(\Sigma_j\) depends on \(j\).
Fixed effects are calculated on life-expectancy at birth (\(e_0\)) as projected by the UN-population division WPP2019 for years 2000 to 2025, and a socio-demographic index (described below). A random effect is determined according to the region the country is in and the correlation on time is assumed to follow the time-series methods described above (AR(1) and RW(2)).
Demographic Index covariate
Baselines and projections for the 40 GHO indicators as well as the additional 9 UHC are either determined by the technical programmes or by the DDI. To build a systematic approach to infilling and extrapolating estimates for all countries we can use time as the main predictor as well as other social and demographic indices. A composite covariate developed for this report is a version of the Socio-demographic Index (SDI) that was originally developed for the 2015 Global Burden of Disease study. This development status indicator has components that are strongly correlated with health outcomes. It is the geometric mean of normalised 0 to 1 indices of total fertility rate, mean years of schooling for those aged 15 and older, and lag distributed income per capita. Instead of using the GBD2017 SDI (some of the input data for this measure are not publically available e.g. fertility), we construct the metric using publicly available fertility data from WPP2019 as well as GDP per capita and education data from the UN Human Development Reports. In the plot below, we compare this constructed SDI to the one from the GBD.
In general, there is strong agreement, between the SDI measure from the public data vs the one from the GBD. There are some missing values for a few countries in the public data and these can be imputed using a spline log-linear model that predicts the sdi.pub using the sdi.gbd and time. A summary of the regression results for such a model is shown below.
| sdi.pub | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 0.28 | 0.28 – 0.29 | <0.001 |
|
region [Europe & Central Asia] |
-0.02 | -0.03 – -0.02 | <0.001 |
|
region [Latin America & Caribbean] |
0.03 | 0.02 – 0.03 | <0.001 |
|
region [Middle East & North Africa] |
0.03 | 0.02 – 0.03 | <0.001 |
| region [North America] | 0.03 | 0.02 – 0.04 | <0.001 |
| region [South Asia] | 0.02 | 0.01 – 0.03 | <0.001 |
|
region [Sub-Saharan Africa] |
0.01 | 0.01 – 0.02 | <0.001 |
| Smooth term (sdi.gbd) | 6.30 | <0.001 | |
| Smooth term (lt) | 1.66 | 0.086 | |
| Observations | 2850 | ||
| R2 | 0.948 | ||
Forecasting the SDI covariate
One of the main advantages of a covariate model is that it can be used to infill missing values in the retrospective data and once projected to the target year, it can be used to increase the robustness of any forecasts of the indicators. The SDI estimates for each country are forecasted using GAMS. Plots of the country and region specific SDI estimates and forecasts can be seen below.
Show SDI forecasts Hide SDI forecasts
